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Item Details
| Title:
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DATA ANALYSIS USING REGRESSION AND MULTILEVEL/HIERARCHICAL MODELS
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| By: |
Andrew Gelman, Jennifer Hill |
| Format: |
Paperback |
| List price:
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£54.00 |
| Our price: |
£47.25 |
| Discount: |
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| You save:
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£6.75 |
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| ISBN 10: |
052168689X |
| ISBN 13: |
9780521686891 |
| Availability: |
Usually dispatched within 3-5 days.
 Delivery
rates
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| Stock: |
Currently 2 available |
| Publisher: |
CAMBRIDGE UNIVERSITY PRESS |
| Pub. date: |
18 December, 2006 |
| Series: |
Analytical Methods for Social Research |
| Pages: |
648 |
| Description: |
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. |
| Synopsis: |
Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. |
| Illustrations: |
160 exercises |
| Publication: |
UK |
| Imprint: |
Cambridge University Press |
| Returns: |
Returnable |
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